Predicting lung exposure of intramuscular niclosamide as an antiviral agent: Power-law based pharmacokinetic modeling.

Niclosamide, a potent anthelmintic agent, has emerged as a candidate against COVID-19 in recent studies. Its formulation has been investigated extensively to address challenges related to systemic exposure. In this study, niclosamide was formulated as a long-acting intramuscular injection to achieve systemic exposure in the lungs for combating the virus. To establish the dose-exposure relationship, a hamster model was selected, given its utility in previous COVID-19 infection studies. Pharmacokinetic (PK) analysis was performed using NONMEM and PsN. Hamsters were administered doses of 55, 96, 128, and 240 mg/kg with each group comprising five animals. Two types of PK models were developed, linear models incorporating partition coefficients and power-law distributed models, to characterize the relationship between drug concentrations in the plasma and lungs of the hamsters. Numerical and visual diagnostics, including basic goodness-of-fit and visual predictive checks, were employed to assess the models. The power-law-based PK model not only demonstrated superior numerical performance compared with the linear model but also exhibited better agreement in visual diagnostic evaluations. This phenomenon was attributed to the nonlinear relationship between drug concentrations in the plasma and lungs, reflecting kinetic heterogeneity. Dose optimization, based on predicting lung exposure, was conducted iteratively across different drug doses, with the minimum effective dose estimated to be ~1115 mg/kg. The development of a power-law-based PK model proved successful and effectively captured the nonlinearities observed in this study. This method is expected to be applicable for investigating the drug disposition of specific formulations in the lungs.

Absorption Distribution Metabolism Excretion and Toxicity Property Prediction Utilizing a Pre-Trained Natural Language Processing Model and Its Applications in Early-Stage Drug Development.

Machine learning techniques are extensively employed in drug discovery, with a significant focus on developing QSAR models that interpret the structural information of potential drugs. In this study, the pre-trained natural language processing (NLP) model, ChemBERTa, was utilized in the drug discovery process. We proposed and evaluated four core model architectures as follows: deep neural network (DNN), encoder, concatenation (concat), and pipe. The DNN model processes physicochemical properties as input, while the encoder model leverages the simplified molecular input line entry system (SMILES) along with NLP techniques. The latter two models, concat and pipe, incorporate both SMILES and physicochemical properties, operating in parallel and with sequential manners, respectively. We collected 5238 entries from DrugBank, including their physicochemical properties and absorption, distribution, metabolism, excretion, and toxicity (ADMET) features. The models' performance was assessed by the area under the receiver operating characteristic curve (AUROC), with the DNN, encoder, concat, and pipe models achieved 62.4%, 76.0%, 74.9%, and 68.2%, respectively. In a separate test with 84 experimental microsomal stability datasets, the AUROC scores for external data were 78% for DNN, 44% for the encoder, and 50% for concat, indicating that the DNN model had superior predictive capabilities for new data. This suggests that models based on structural information may require further optimization or alternative tokenization strategies. The application of natural language processing techniques to pharmaceutical challenges has demonstrated promising results, highlighting the need for more extensive data to enhance model generalization.

A Bayesian spatiotemporal approach to modelling arboviral diseases in Mexico.

BACKGROUND: The objective of this study was to evaluate the spatial and temporal patterns of disease prevalence clusters of dengue (DENV), chikungunya (CHIKV) and Zika (ZIKV) virus and how socio-economic and climatic variables simultaneously influence the risk and rate of occurrence of infection in Mexico. METHODS: To determine the spatiotemporal clustering and the effect of climatic and socio-economic covariates on the rate of occurrence of disease and risk in Mexico, we applied correlation methods, seasonal and trend decomposition using locally estimated scatterplot smoothing, hotspot analysis and conditional autoregressive Bayesian models. RESULTS: We found cases of the disease are decreasing and a significant association between DENV, CHIKV and ZIKV cases and climatic and socio-economic variables. An increment of cases was identified in the northeastern, central west and southeastern regions of Mexico. Climatic and socio-economic covariates were significantly associated with the rate of occurrence and risk of the three arboviral disease cases. CONCLUSION: The association of climatic and socio-economic factors is predominant in the northeastern, central west and southeastern regions of Mexico. DENV, CHIKV and ZIKV cases showed an increased risk in several states in these regions and need urgent attention to allocate public health resources to the most vulnerable regions in Mexico.

Integrative Korean Medicine Treatments for Traumatic Facial Palsy Following Mandibular Fracture: A Case Report and Literature Review.

Prior studies exploring the effectiveness of traditional Korean medicine (TKM) treatment for facial palsy have mainly focused on Bell's palsy, and there are few studies on the effectiveness of TKM treatments for traumatic facial palsy following mandibular fracture. The patient was a 24-year-old Korean man with left-sided facial paralysis following a left mandibular fracture. Surgery was performed for the fracture and the facial palsy was treated using conventional medicine (CM) treatments for approximately 3 months, but there was no improvement observed in the patient's condition. Subsequently, the patient underwent an integrative Korean medicine treatment regimen consisting of acupuncture, pharmacopuncture, cupping, moxibustion, and herbal medication for a duration of 2 months. After 2 months of treatments, the House-Brackmann facial grading scale changed from Ⅴ to II and Yanagihara's unweighted grading score increased from 9 to 34. This case presentation and previous studies of traumatic facial palsy using TKM treatment show that TKM treatment may be considered a complementary or alternative treatment method to CM treatment in patients with traumatic facial palsy. PROSPERO registration number: CRD42023445051.

Epidemiological Characteristics of a COVID-19 Outbreak in a Psychiatric Hospital in Chung-buk.

This study investigated the causes and risks for infection spread in three psychiatric hospitals in Chung-buk, South Korea, to strategize measures to block transmission and prevent a large-scale epidemic. From December 2020 to January 2021, 358 inpatients of Psychiatric Hospitals A, B, and C were enrolled to identify the epidemiological characteristics of confirmed patients. Epidemic curves and propagation relationships were constructed and a genotype analysis was conducted. The index case inpatient from Hospital A transmitted the infection to patients in Hospitals B and C; the infection was confirmed in 47, 193, and 118 patients in Hospitals A, B, and C, respectively. The patient characteristics hampered communication and the close identification of symptom onset. The incidence rate was 10 (2.9%) among employees and 348 (35.8%) among inpatients. The relative risk was 12.1 (95% CI: 6.6-22.5) times higher among inpatients than employees. Next-generation sequencing confirmed the probable infection source as a genotype identical to that of two different outbreaks, although the infection spread was undetermined. Direct risk factors emerged from patient characteristics, wherein cohort isolation was meaningless due to uncontrolled communication. Indirect risk factors included hospital-specific problems due to external factors (non-patient system deficiencies or employee negligence). Prior inspections, a confirmation of non-infection, and institutional emergent measures are needed.

Liquid metal-based soft, hermetic, and wireless-communicable seals for stretchable systems.

Soft materials tend to be highly permeable to gases, making it difficult to create stretchable hermetic seals. With the integration of spacers, we demonstrate the use of liquid metals, which show both metallic and fluidic properties, as stretchable hermetic seals. Such soft seals are used in both a stretchable battery and a stretchable heat transfer system that involve volatile fluids, including water and organic fluids. The capacity retention of the battery was ~72.5% after 500 cycles, and the sealed heat transfer system showed an increased thermal conductivity of approximately 309 watts per meter-kelvin while strained and heated. Furthermore, with the incorporation of a signal transmission window, we demonstrated wireless communication through such seals. This work provides a route to create stretchable yet hermetic packaging design solutions for soft devices.

Fractal Kinetic Implementation in Population Pharmacokinetic Modeling.

Compartment modeling is a widely accepted technique in the field of pharmacokinetic analysis. However, conventional compartment modeling is performed under a homogeneity assumption that is not a naturally occurring condition. Since the assumption lacks physiological considerations, the respective modeling approach has been questioned, as novel drugs are increasingly characterized by physiological or physical features. Alternative approaches have focused on fractal kinetics, but evaluations of their application are lacking. Thus, in this study, a simulation was performed to identify desirable fractal-kinetics applications in conventional modeling. Visible changes in the profiles were then investigated. Five cases of finalized population models were collected for implementation. For model diagnosis, the objective function value (OFV), Akaike's information criterion (AIC), and corrected Akaike's information criterion (AICc) were used as performance metrics, and the goodness of fit (GOF), visual predictive check (VPC), and normalized prediction distribution error (NPDE) were used as visual diagnostics. In most cases, model performance was enhanced by the fractal rate, as shown in a simulation study. The necessary parameters of the fractal rate in the model varied and were successfully estimated between 0 and 1. GOF, VPC, and NPDE diagnostics show that models with the fractal rate described the data well and were robust. In the simulation study, the fractal absorption process was, therefore, chosen for testing. In the estimation study, the rate application yielded improved performance and good prediction-observation agreement in early sampling points, and did not cause a large shift in the original estimation results. Thus, the fractal rate yielded explainable parameters by setting only the heterogeneity exponent, which reflects true physiological behavior well. This approach can be expected to provide useful insights in pharmacological decision making.

An Exploratory Study of Medical Journal's Twitter Use: Metadata, Networks, and Content Analyses.

BACKGROUND: An increasing number of medical journals are using social media to promote themselves and communicate with their readers. However, little is known about how medical journals use Twitter and what their social media management strategies are. OBJECTIVE: This study aimed to understand how medical journals use Twitter from a global standpoint. We conducted a broad, in-depth analysis of all the available Twitter accounts of medical journals indexed by major indexing services, with a particular focus on their social networks and content. METHODS: The Twitter profiles and metadata of medical journals were analyzed along with the social networks on their Twitter accounts. RESULTS: The results showed that overall, publishers used different strategies regarding Twitter adoption, Twitter use patterns, and their subsequent decisions. The following specific findings were noted: journals with Twitter accounts had a significantly higher number of publications and a greater impact than their counterparts; subscription journals had a slightly higher Twitter adoption rate (2%) than open access journals; journals with higher impact had more followers; and prestigious journals rarely followed other lesser-known journals on social media. In addition, an in-depth analysis of 2000 randomly selected tweets from 4 prestigious journals revealed that The Lancet had dedicated considerable effort to communicating with people about health information and fulfilling its social responsibility by organizing committees and activities to engage with a broad range of health-related issues; The New England Journal of Medicine and the Journal of the American Medical Association focused on promoting research articles and attempting to maximize the visibility of their research articles; and the British Medical Journal provided copious amounts of health information and discussed various health-related social problems to increase social awareness of the field of medicine. CONCLUSIONS: Our study used various perspectives to investigate how medical journals use Twitter and explored the Twitter management strategies of 4 of the most prestigious journals. Our study provides a detailed understanding of medical journals' use of Twitter from various perspectives and can help publishers, journals, and researchers to better use Twitter for their respective purposes.

Suicidality Detection on Social Media Using Metadata and Text Feature Extraction and Machine Learning.

In this study, we implemented machine learning models that can detect suicidality posts on Twitter. We randomly selected and annotated 20,000 tweets and explored metadata and text features to build effective models. Metadata features were studied in great details to understand their possibility and importance in suicidality detection models. Results showed that posting type (i.e., reply or not) and time-related features such as the month, day of the week, and the time (AM vs. PM) were the most important metadata features in suicidality detection models. Specifically, the probability of a social media post being suicidal is higher if the post is a reply to other users rather than an original tweet. Moreover, tweets created in the afternoon, on Fridays and weekends, and in fall have higher probabilities of being detected as suicidality tweets compared with those created in other times. By integrating metadata and text features, we obtained a model of good performance (i.e., F1 score of 0.846) that can assist humans in the real-world setting to detect suicidality social media posts.

MR-self Noise2Noise: self-supervised deep learning-based image quality improvement of submillimeter resolution 3D MR images.

OBJECTIVES: The study aimed to develop a deep neural network (DNN)-based noise reduction and image quality improvement by only using routine clinical scans and evaluate its performance in 3D high-resolution MRI. METHODS: This retrospective study included T1-weighted magnetization-prepared rapid gradient-echo (MP-RAGE) images from 185 clinical scans: 135 for DNN training, 11 for DNN validation, 20 for qualitative evaluation, and 19 for quantitative evaluation. Additionally, 18 vessel wall imaging (VWI) data were included to evaluate generalization. In each scan of the DNN training set, two noise-independent images were generated from the k-space data, resulting in an input-label pair. 2.5D U-net architecture was utilized for the DNN model. Qualitative evaluation between conventional MP-RAGE and DNN-based MP-RAGE was performed by two radiologists in image quality, fine structure delineation, and lesion conspicuity. Quantitative evaluation was performed with full sampled data as a reference by measuring quantitative error metrics and volumetry at 7 different simulated noise levels. DNN application on VWI was evaluated by two radiologists in image quality. RESULTS: Our DNN-based MP-RAGE outperformed conventional MP-RAGE in all image quality parameters (average scores = 3.7 vs. 4.9, p < 0.001). In the quantitative evaluation, DNN showed better error metrics (p < 0.001) and comparable (p > 0.09) or better (p < 0.02) volumetry results than conventional MP-RAGE. DNN application to VWI also revealed improved image quality (3.5 vs. 4.6, p < 0.001). CONCLUSION: The proposed DNN model successfully denoises 3D MR image and improves its image quality by using routine clinical scans only. KEY POINTS: • Our deep learning framework successfully improved conventional 3D high-resolution MRI in all image quality parameters, fine structure delineation, and lesion conspicuity. • Compared to conventional MRI, the proposed deep neural network-based MRI revealed better quantitative error metrics and comparable or better volumetry results. • Deep neural network application to 3D MRI whose pulse sequences and parameters were different from the training data showed improvement in image quality, revealing the potential to generalize on various clinical MRI.

Solvent-Assisted Filling of Liquid Metal and Its Selective Dewetting for the Multilayered 3D Interconnect in Stretchable Electronics.

As stretchable electronics are rapidly developing and becoming complex, the requirement for stretchable, multilayered, and large-area printed circuit boards (PCBs) is emerging. This demands a stretchable electrode and its vertical interconnect access (via) for 3-dimensional (3D) connectivity between layers. Here, we demonstrate solvent-assisted liquid metal (LM) filling into the submicrometer channel (∼400 nm), including via-hole filling and selective dewetting of LM. We provide the theoretical background of solvent-assisted LM filling and selective dewetting and reveal the osmotic pressure arising from anomalous mass transport phenomena, case II diffusion, which drives negative pressure, the spontaneous pulling of LM into the open channel. Also, we suggest design criteria for the geometry and dimension of LM interconnects to obtain structural stability without dewetting, based on the theoretical and computational background. We demonstrate a simple stretchable near-field communication (NFC) device including transferred micrometer-size light-emitting diodes (LEDs) with only 230 µm to the stretchable liquid metal PCB, without any soldering process. The device operates stably under repetitive stretching and releasing (∼50% uniaxial strain) due to the stable connection through the LM via between the upper and lower layers. Finally, we propose a concept for modular-type stretchable electronics, based on the cohesive liquid nature of LM. As a building block, the functional module can be easily removed from a mainframe, and replaced by another functional module, to suit user demand.

Effect of Chitosan Coating for Efficient Encapsulation and Improved Stability under Loading Preparation and Storage Conditions of Bacillus Lipopeptides.

This study aims to evaluate the effect of chitosan coating on the formation and properties of Bacillus cyclic lipopeptide (CLP)-loaded liposomes. A nanoencapsulation strategy for a chitosan-coated liposomal system using lecithin phospholipids for the entrapment of antibiotic CLP prepared from Bacillus subtilis KB21 was developed. The produced chitosan-coated CLP liposome had mean size in the range of 118.47-121.67 nm. Transmission electron microscopy showed the spherical-shaped vesicles. Fourier transform infrared spectroscopy findings indicated the successful coating of the produced CLP-loaded liposomes by the used chitosan. Liposomes coated with 0.2% and 0.5% chitosan concentration decreased the surface tension by 7.3-12.1%, respectively, and increased the CLP content by 15.1-27.0%, respectively, compared to the uncoating liposomes. The coated concentration of chitosan influenced their CLP loading encapsulation efficiency and release kinetics. The physicochemical results of the dynamic light scattering, CLP capture efficiency and long-term storage capacity of nanocapsules increased with chitosan coating concentration. Furthermore, the chitosan-coated liposomes exhibited a significant enhancement in the stability of CLP loading liposomes. These results may suggest the potential application of chitosan-coated liposomes as a carrier of antibiotics in the development of the functional platform.

Cuticular pad-inspired selective frequency damper for nearly dynamic noise-free bioelectronics.

Bioelectronics needs to continuously monitor mechanical and electrophysiological signals for patients. However, the signals always include artifacts by patients' unexpected movement (such as walking and respiration under approximately 30 hertz). The current method to remove them is a signal process that uses a bandpass filter, which may cause signal loss. We present an unconventional bandpass filter material-viscoelastic gelatin-chitosan hydrogel damper, inspired by the viscoelastic cuticular pad in a spider-to remove dynamic mechanical noise artifacts selectively. The hydrogel exhibits frequency-dependent phase transition that results in a rubbery state that damps low-frequency noise and a glassy state that transmits the desired high-frequency signals. It serves as an adaptable passfilter that enables the acquisition of high-quality signals from patients while minimizing signal process for advanced bioelectronics.

Understanding the Research Landscape of Deep Learning in Biomedical Science: Scientometric Analysis.

BACKGROUND: Advances in biomedical research using deep learning techniques have generated a large volume of related literature. However, there is a lack of scientometric studies that provide a bird's-eye view of them. This absence has led to a partial and fragmented understanding of the field and its progress. OBJECTIVE: This study aimed to gain a quantitative and qualitative understanding of the scientific domain by analyzing diverse bibliographic entities that represent the research landscape from multiple perspectives and levels of granularity. METHODS: We searched and retrieved 978 deep learning studies in biomedicine from the PubMed database. A scientometric analysis was performed by analyzing the metadata, content of influential works, and cited references. RESULTS: In the process, we identified the current leading fields, major research topics and techniques, knowledge diffusion, and research collaboration. There was a predominant focus on applying deep learning, especially convolutional neural networks, to radiology and medical imaging, whereas a few studies focused on protein or genome analysis. Radiology and medical imaging also appeared to be the most significant knowledge sources and an important field in knowledge diffusion, followed by computer science and electrical engineering. A coauthorship analysis revealed various collaborations among engineering-oriented and biomedicine-oriented clusters of disciplines. CONCLUSIONS: This study investigated the landscape of deep learning research in biomedicine and confirmed its interdisciplinary nature. Although it has been successful, we believe that there is a need for diverse applications in certain areas to further boost the contributions of deep learning in addressing biomedical research problems. We expect the results of this study to help researchers and communities better align their present and future work.

Highly accelerated 3D MPRAGE using deep neural network-based reconstruction for brain imaging in children and young adults.

OBJECTIVES: This study aimed to accelerate the 3D magnetization-prepared rapid gradient-echo (MPRAGE) sequence for brain imaging through the deep neural network (DNN). METHODS: This retrospective study used the k-space data of 240 scans (160 for the training set, mean ± standard deviation age, 93 ± 80 months, 94 males; 80 for the test set, 106 ± 83 months, 44 males) of conventional MPRAGE (C-MPRAGE) and 102 scans (77 ± 74 months, 52 males) of both C-MPRAGE and accelerated MPRAGE. All scans were acquired with 3T scanners. DNN was developed with simulated-acceleration data generated by under-sampling. Quantitative error metrics were compared between images reconstructed with DNN, GRAPPA, and E-SPIRIT using the paired t-test. Qualitative image quality was compared between C-MPRAGE and accelerated MPRAGE reconstructed with DNN (DNN-MPRAGE) by two readers. Lesions were segmented and the agreement between C-MPRAGE and DNN-MPRAGE was assessed using linear regression. RESULTS: Accelerated MPRAGE reduced scan times by 38% compared to C-MPRAGE (142 s vs. 320 s). For quantitative error metrics, DNN showed better performance than GRAPPA and E-SPIRIT (p < 0.001). For qualitative evaluation, overall image quality of DNN-MPRAGE was comparable (p > 0.999) or better (p = 0.025) than C-MPRAGE, depending on the reader. Pixelation was reduced in DNN-MPRAGE (p < 0.001). Other qualitative parameters were comparable (p > 0.05). Lesions in C-MPRAGE and DNN-MPRAGE showed good agreement for the dice similarity coefficient (= 0.68) and linear regression (R2 = 0.97; p < 0.001). CONCLUSIONS: DNN-MPRAGE reduced acquisition time by 38% and revealed comparable image quality to C-MPRAGE. KEY POINTS: • DNN-MPRAGE reduced acquisition times by 38%. • DNN-MPRAGE outperformed conventional reconstruction on accelerated scans (SSIM of DNN-MPRAGE = 0.96, GRAPPA = 0.43, E-SPIRIT = 0.88; p < 0.001). • Compared to C-MPRAGE scans, DNN-MPRAGE showed improved mean scores for overall image quality (2.46 vs. 2.52; p < 0.001) or comparable perceived SNR (2.56 vs. 2.58; p = 0.08).

Cerebrospinal Fluid-philic and Biocompatibility-Enhanced Soft Cranial Window for Long-Term In Vivo Brain Imaging.

Soft, transparent poly(dimethyl siloxane) (PDMS)-based cranial windows in animal models have created many opportunities to investigate brain functions with multiple in vivo imaging modalities. However, due to the hydrophobic nature of PDMS, the wettability by cerebrospinal fluid (CSF) is poor, which may cause air bubble trapping beneath the window during implantation surgery, and favorable heterogeneous bubble nucleation at the interface between hydrophobic PDMS and CSF. This may result in excessive growth of the entrapped bubble under the soft cranial window. Herein, to yield biocompatibility-enhanced, trapped bubble-minimized, and soft cranial windows, this report introduces a CSF-philic PDMS window coated with hydroxyl-enriched poly(vinyl alcohol) (PVA) for long-term in vivo imaging. The PVA-coated PDMS (PVA/PDMS) film exhibits a low contact angle θACA (33.7 ± 1.9°) with artificial CSF solution and maintains sustained CSF-philicity. The presence of the PVA layer achieves air bubble-free implantation of the soft cranial window, as well as induces the formation of a thin wetting film that shows anti-biofouling performance through abundant water molecules on the surface, leading to long-term optical clarity. In vivo studies on the mice cortex verify that the soft and CSF-philic features of the PVA/PDMS film provide minimal damage to neuronal tissues and attenuate immune response. These advantages of the PVA/PDMS window are strongly correlated with the enhancement of cortical hemodynamic changes and the local field potential recorded through the PVA/PDMS film, respectively. This collection of results demonstrates the potential for future microfluidic platforms for minimally invasive CSF extraction utilizing a CSF-philic fluidic passage.

Understanding information behavior of South Korean Twitter users who express suicidality on Twitter.

Objective: Although there were few studies on how suicidal users behave on Twitter, they only investigated partial aspects such as tweeting frequency and tweet length. Therefore, we aim to understand the various information behavior of suicidal users in South Korea. Methods: To achieve this goal, we annotated 20,000 tweets and identified 1097 tweets with the expression of suicidality (i.e. suicidal tweets) and 229 suicidal users (i.e. experimental group). Using the data, a user profile analysis, comparative analysis with control group, and tweets/hashtags analysis were performed. Results: Our results show that many suicidal users used suicide-related keywords in their user IDs, usernames, descriptions, and pinned tweets. We also found that, compared to the control group, the experimental group show different patterns of information behavior. The experimental group did not frequently use Twitter and, on average, wrote longer texts than the control group. A clear seasonal pattern was also identified in the experimental group's tweeting behavior. Frequently used keywords/hashtags were extracted from tweets written by the experimental group for the purpose of understanding their concerns and detecting more suicidal tweets. Conclusions: We believe that our study will help in the understanding of suicidal users' information behavior on social media and lay the basis for more accurate actions for suicide prevention and early intervention on social media.

Convolutional neural network-based reconstruction for acceleration of prostate T2 weighted MR imaging: a retro- and prospective study.

OBJECTIVE: The aim of this study was to develop a deep neural network (DNN)-based parallel imaging reconstruction for highly accelerated 2D turbo spin echo (TSE) data in prostate MRI without quality degradation compared to conventional scans. METHODS: 155 participant data were acquired for training and testing. Two DNN models were generated according to the number of acquisitions (NAQ) of input images: DNN-N1 for NAQ = 1 and DNN-N2 for NAQ = 2. In the test data, DNN and TSE images were compared by quantitative error metrics. The visual appropriateness of DNN reconstructions on accelerated scans (DNN-N1 and DNN-N2) and conventional scans (TSE-Conv) was assessed for nine parameters by two radiologists. The lesion detection was evaluated at DNNs and TES-Conv by prostate imaging-reporting and data system. RESULTS: The scan time was reduced by 71% at NAQ = 1, and 42% at NAQ = 2. Quantitative evaluation demonstrated the better error metrics of DNN images (29-43% lower NRMSE, 4-13% higher structure similarity index, and 2.8-4.8 dB higher peak signal-to-noise ratio; p < 0.001) than TSE images. In the assessment of the visual appropriateness, both radiologists evaluated that DNN-N2 showed better or comparable performance in all parameters compared to TSE-Conv. In the lesion detection, DNN images showed almost perfect agreement (κ > 0.9) scores with TSE-Conv. CONCLUSIONS: DNN-based reconstruction in highly accelerated prostate TSE imaging showed comparable quality to conventional TSE. ADVANCES IN KNOWLEDGE: Our framework reduces the scan time by 42% of conventional prostate TSE imaging without sequence modification, revealing great potential for clinical application.

Model-Based Equivalent Dose Optimization to Develop New Donepezil Patch Formulation.

Donepezil patch was developed to replace the original oral formulation. To accurately describe the pharmacokinetics of donepezil and investigate compatible doses between two formulations, a population pharmacokinetic model for oral and transdermal patches was built based on a clinical study. Plasma donepezil levels were analyzed via liquid chromatography/tandem mass spectrometry. Non-compartmental analyses were performed to derive the initial parameters for compartmental analyses. Compartmental analysis (CA) was performed with NLME software NONMEM assisted by Perl-speaks-NONMEM, and R. Model evaluation was proceeded via visual predictive checks (VPC), goodness-of-fit (GOF) plotting, and bootstrap method. The bioequivalence test was based on a 2 × 2 crossover design, and parameters of AUC and Cmax were considered. We found that a two-compartment model featuring two transit compartments accurately describes the pharmacokinetics of nine subjects administered in oral, as well as of the patch-dosed subjects. Through evaluation, the model was proven to be sufficiently accurate and suitable for further bioequivalence tests. Based on the bioequivalence test, 114 mg/101.3 cm2-146 mg/129.8 cm2 of donepezil patch per week was equivalent to 10 mg PO donepezil per day. In conclusion, the pharmacokinetic model was successfully developed, and acceptable parameters were estimated. However, the size calculated by an equivalent dose of donepezil patch could be rather large. Further optimization in formulation needs to be performed to find appropriate usability in clinical situations.